A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter

doi:10.16383/j.aas.2016.c150182

	A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter
	Chen, Hui1,2 ; Han, Chong-Zhao 1
	2016
发表期刊	Zidonghua Xuebao/Acta Automatica Sinica
ISSN	02544156
卷号	42 期号:1 页码:26-36
摘要	To improve the effectiveness of particle sampling in the sequential Monte Carlo (SMC) implementation of the multi-Bernoulli filter, this paper proposes a new SMC implementation of the CBMeMBer filter using the so called auxiliary particle filter (APF). First, according to the posterior multi-Bernoulli density, this paper redefines the sampling problem by introducing some auxiliary random variables suited to the CBMeMBer filter. The measurement and the prior density component are chosen accordingly as auxiliary variables. As a result, this method can sample particles concentrating on the high likelihood state space and the Bernoulli probability density of track corrected by the actual target measurement. Therefore, a more accurate posterior probability density of multi-target multi-Bernoulli (MeMBer) can be obtained. Meanwhile, the sampling distribution functions of those auxiliary random variables and the multi-target states are designed for the legacy tracks and the measurement-corrected tracks. Moreover, this paper corrects iteratively the prior density component based on the progressive correction (PC) algorithm in order to improve the solving accuracy of sampling distribution functions. Finally, simulation results show the effectiveness of the proposed approach as applied to two typical nonlinear tracking problems. Copyright © 2016 Acta Automatica Sinica. All rights reserved.
关键词	Distributed computer systems Distribution functions Iterative methods Monte Carlo methods Random variables Auxiliary variables Multi-Bernoulli Multi-target tracking Random finite set (RFS) Sequential Monte Carlo
DOI	10.16383/j.aas.2016.c150182
收录类别	EI
语种	中文
出版者	Science Press
EI入藏号	20160601889395
EI主题词	Target tracking
EI分类号	921.6 Numerical Methods - 922.1 Probability Theory - 922.2 Mathematical Statistics
来源库	Compendex
分类代码	722.4 Digital Computers and Systems - 921.6 Numerical Methods - 922.1 Probability Theory - 922.2 Mathematical Statistics
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/112773
专题	电气工程与信息工程学院
作者单位	1.Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an; 710049, China; 2.School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Chen, Hui,Han, Chong-Zhao. A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter[J]. Zidonghua Xuebao/Acta Automatica Sinica,2016,42(1):26-36.
APA	Chen, Hui,&Han, Chong-Zhao.(2016).A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter.Zidonghua Xuebao/Acta Automatica Sinica,42(1),26-36.
MLA	Chen, Hui,et al."A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter".Zidonghua Xuebao/Acta Automatica Sinica 42.1(2016):26-36.